Cross-disciplinary research: What configurations of fields of science are found in grant proposals today? is a research paper published in Research Evaluation (2014). On theSindex it has a DataRank of 1.2. It has been cited 21 times, with 15 citing works in its 1-hop citation network.
<p>Considering the complexity of the world problems, it seems evident that they do not fit straightforwardly into a disciplinary framework. In this context, the question arises as to whether and how frequently several disciplines cooperate on research projects. Cross-disciplinary cooperation in research might be difficult for two reasons. On one hand, many researchers feel that efforts to achieve methodological rigour, exactness, and control are only possible in the circumscribed area of a discipline. On the other hand, it is claimed that funding organizations, with their rigid disciplinary classification systems, impede cross-disciplinary research in the context of their selection and evaluation procedures. For a total of N = 8,496 grant proposals submitted to the Austrian Science Fund (FWF) from 1999 to 2009, detailed codings of the subdisciplines involved were available for the statistical analysis. Latent class analysis produced 12 latent classes or configurations of fields of science. Mono-disciplinary projects are very well represented in physics/astronomy/mechanics, geosciences, and clinical medicine. Cross-disciplinarity is found particularly in research project proposals of fields of science with clearly overlapping content (e.g. preclinical and clinical medicine) and mainly in research proposals submitted by fields of science within the humanities and social sciences.</p>
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.464
From this paper's citation signal
Citation Network Contribution
0.722
From 13 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 39% comes from its base citations and 61% from the citation network (13 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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